Risk Modeling, Assessment, and Management (4^th Ed.)
Wiley Series in Systems Engineering and Management Series

Presents systems-based theory, methodology, and applications in risk modeling, assessment, and management

This book examines risk analysis, focusing on quantifying risk and constructing probabilities for real-world decision-making, including engineering, design, technology, institutions, organizations, and policy. The author presents fundamental concepts (hierarchical holographic modeling; state space; decision analysis; multi-objective trade-off analysis) as well as advanced material (extreme events and the partitioned multi-objective risk method; multi-objective decision trees; multi-objective risk impact analysis method; guiding principles in risk analysis); avoids higher mathematics whenever possible; and reinforces the material with examples and case studies. The book will be used in systems engineering, enterprise risk management, engineering management, industrial engineering, civil engineering, and operations research.

The fourth edition of Risk Modeling, Assessment, and Management features:

• Expanded chapters on systems-based guiding principles for risk modeling, planning, assessment, management, and communication; modeling interdependent and interconnected complex systems of systems with phantom system models; and hierarchical holographic modeling
• An expanded appendix including a Bayesian analysis for the prediction of chemical carcinogenicity, and the Farmer?s Dilemma formulated and solved using a deterministic linear model
• Updated case studies including a new case study on sequential Pareto-optimal decisions for emergent complex systems of systems
• A new companion website with over 200 solved exercises that feature risk analysis theories, methodologies, and application

Risk Modeling, Assessment, and Management, Fourth Edition, is written for both undergraduate and graduate students in systems engineering and systems management courses. The text also serves as a resource for academic, industry, and government professionals in the fields of homeland and cyber security, healthcare, physical infrastructure systems, engineering, business, and more.

Preface to the Fourth Edition ix

The Companion Website xv

Acknowledgments xvii

Part I Fundamentals of Risk Modeling, Assessment, and Management 1

1 The Art and Science of Systems and Risk Analysis 3

1.1 Introduction 3

1.2 Systems Engineering 4

1.3 Risk Assessment and Management 14

1.4 Concept Road Map 26

1.5 Epilogue 35

References 35

2 The Role of Modeling in the Definition and Quantification of the Risk Function 41

2.1 Introduction 41

2.2 The Risk Assessment and Management Process: Historical Perspectives 43

2.3 Information, Intelligence, and Models 45

2.4 The Building Blocks of Mathematical Models 47

2.5 On the Complex Definition of Risk, Vulnerability, and Resilience: a Systems‐Based Approach 51

2.6 On the Definition of Vulnerabilities in Measuring Risks to Systems 56

2.7 On the Definition of Resilience in Measuring Risk to Systems 57

2.8 On the Complex Quantification of Risk to Systems 60

References 65

3 Identifying Risk through Hierarchical Holographic Modeling and its Derivatives 69

3.1 Hierarchical Aspects 69

3.2 Hierarchical Overlapping Coordination 70

3.3 HHM 73

3.4 HHM and the Theory of Scenario Structuring 76

3.5 Adaptive Multiplayer HHM Game 79

3.6 Water Resources System 80

3.7 Sustainable Development 83

3.8 HHM in a System Acquisition Project 86

3.9 Software Acquisition 90

3.10 Hardening the Water Supply Infrastructure 94

3.11 Risk Assessment and Management for Support of Operations other than War 98

3.12 Automated Highway System 103

3.13 Food‐Poisoning Scenarios 108

References 113

4 Modeling and Decision Analysis 115

4.1 Introduction 115

4.2 Decision Rules Under Uncertainty 116

4.3 Decision Trees 118

4.4 Decision Matrix 122

4.5 The Fractile Method 124

4.6 Triangular Distribution 127

4.7 Influence Diagrams 128

4.8 Population Dynamic Models 132

4.9 PSM 139

4.10 Example Problems 144

References 152

5 Multiobjective Trade‐off Analysis 155

5.1 Introduction 155

5.2 Examples of Multiple Environmental Objectives 157

5.3 The Surrogate Worth Trade‐off Method 159

5.4 Characterizing a Proper Noninferior Solution 166

5.5 The SWT Method and the Utility Function Approach 168

5.6 Example Problems 172

5.7 Summary 177

References 178

6 Defining Uncertainty and Sensitivity Analysis 179

6.1 Introduction 179

6.2 Sensitivity, Responsivity, Stability, and Irreversibility 180

6.3 Uncertainties Due to Errors in Modeling 182

6.4 Characterization of Modeling Errors 183

6.5 Uncertainty Taxonomy 185

6.6 The USIM 196

6.7 Formulation of the Multiobjective Optimization Problem 199

6.8 A Robust Algorithm of the USIM 204

6.9 Integration of the USIM with Parameter Optimization at the Design Stage 207

6.10 Conclusions 209

References 209

7 Risk Filtering, Ranking, and Management 211

7.1 Introduction 211

7.2 Past Efforts in Risk Filtering and Ranking 212

7.3 RFRM: A Methodological Framework 213

7.4 Case Study: An OOTW 220

7.5 Summary 224

References 224

Part II Advances in Risk Modeling, Assessment, and Management 227

8 Risk of Extreme Events and the Fallacy of the Expected Value 229

8.1 Introduction 229

8.2 Risk of Extreme Events 230

8.3 The Fallacy of the Expected Value 232

8.4 The PMRM 233

8.5 General Formulation of the PMRM 236

8.6 Summary of the Pmrm 238

8.7 Illustrative Example 239

8.8 Analysis of Dam Failure and Extreme Flood through the PMRM 240

8.9 Example Problems 243

8.10 Summary 257

References 257

9 Multiobjective Decision‐tree Analysis 259

9.1 Introduction 259

9.2 Methodological Approach 261

9.3 Differences between SODT and MODT 279

9.4 Summary 281

9.5 Example Problems 282

References 293

10 Multiobjective Risk Impact Analysis Method 295

10.1 Introduction 295

10.2 Impact Analysis 296

10.3 The Multiobjective, Multistage Impact Analysis Method: An Overview 297

10.4 Combining the PMRM and the MMIAM 298

10.5 Relating Multiobjective Decision Trees to the MRIAM 304

10.6 Example Problems 313

10.7 Epilogue 325

References 326

11 Statistics of Extremes: Extension of the PMRM 329

11.1 A Review of the Partitioned Multiobjective Risk Method 329

11.2 Statistics of Extremes 333

11.3 Incorporating the Statistics of Extremes into the PMRM 338

11.4 Sensitivity Analysis of the Approximation of f4(·) 344

11.5 Generalized Quantification of Risk of Extreme Events 350

11.6 Summary 356

11.7 Example Problems 357

References 368

12 Systems‐Based Guiding Principles for Risk Modeling, Planning, Assessment, Management, andCommunication 371

12.1 Introduction 371

12.2 The Journey: The Guiding Principles in the Broader Context of the Emerging Next Generation Developed by the Federal Aviation Administration  372

References 387

13 Fault Trees 389

13.1 Introduction 389

13.2 Basic Fault-Tree Analysis 391

13.3 Reliability and Fault-Tree Analysis 392

13.4 Minimal Cut Sets 397

13.5 The DARE Using Fault Trees 400

13.6 Extreme Events in Fault Tree Analysis 403

13.7 An Example Problem Based on a Case Study 405

13.8 Failure Mode and Effects Analysis and Failure Mode, Effects, and Criticality Analysis 409

13.9 Event Trees 411

13.10 Example Problems 414

References 420

14 Multiobjective Statistical Method 423

14.1 Introduction 423

14.2 Mathematical Formulation of the Interior Drainage Problem 424

14.3 Formulation of the Optimization Problem 424

14.4 The MSM: Step-by-Step 425

14.5 The SWT Method 427

14.6 Multiple Objectives 428

14.7 Applying the MSM 429

14.8 Example Problems 432

References 438

15 Principles and Guidelines for Project Risk Management 439

15.1 Introduction 439

15.2 Definitions and Principles of Project Risk Management 440

15.3 Project Risk Management Methods 443

15.4 Aircraft Development Example 450

15.5 Quantitative Risk Assessment and Management of Software Acquisition 454

15.6 Critical Factors That Affect Software Nontechnical Risk 458

15.7 Basis for Variances in Cost Estimation 460

15.8 Discrete Dynamic Modeling 461

15.9 Summary 469

References 469

16 Modeling Complex Systems of Systems with Phantom System Models 473

16.1 Introduction 473

16.2 What Have We Learned from Other Contributors? 474

16.3 The Centrality of the States of the System in Modeling and in Risk Analysis 476

16.4 The Centrality of Time in Modeling Multidimensional Risk, Uncertainty, and Benefits 477

16.5 Extension of HHM to PSM 478

16.6 PSM and Meta-modeling 480

16.7 PSM Laboratory 486

16.8 Summary 488

References 489

17 Adaptive Two‐Player Hierarchical Holographic Modeling Game for Counterterrorism IntelligenceAnalysis 493

17.1 Introduction 493

17.2 Bayes’ Theorem 494

17.3 Modeling the Multiple Perspectives of Complex Systems 495

17.4 Adaptive Two‐Player Hhm Game: Terrorist Networks versus Homeland Protection 499

17.5 The Building Blocks of Mathematical Models and the Centrality of State Variables in Intelligence Analysis 502

17.6 Hierarchical Adaptive Two‐Player HHM Game 504

17.7 Collaborative Computing Support for Adaptive Two‐Player HHM Games 505

17.8 Summary 507

References 508

18 Inoperability Input–Output Model and Its Derivatives for Interdependent Infrastructure Sectors 511

18.1 Overview 511

18.2 Background: The Original Leontief Input–Output Model 512

18.3 Inoperability Input–Output Model 513

18.4 Regimes of Recovery 516

18.5 Supporting Databases for IIM Analysis 517

18.6 National and Regional Databases for IIM Analysis 518

18.7 RIMS II 522

18.8 Development of the IIM and its Extensions 523

18.9 The Dynamic IIM 527

18.10 Practical Uses of the IIM 530

18.11 Uncertainty IIM 533

18.12 Example Problems 536

18.13 Summary 539

References 540

19 Case Studies 543

19.1 A Risk‐Based Input–Output Methodology for Measuring the Effects of the August 2003 Northeast Blackout  543

19.2 Systemic Valuation of Strategic Preparedness Through Applying the IIM with Lessons Learned from Hurricane Katrina 558

19.3 Ex Post Analysis Using the IIM of the September 11, 2001, Attack on the United States 569

19.4 Risk Modeling, Assessment, and Management of Lahar Flow Threat 575

19.5 The Statistics of Extreme Events and 6‐Sigma Capability 587

19.6 Sequential Pareto‐Optimal Decisions Made During Emergent Complex Systems of Systems: An Application to the Faa Nextgen 593

References 612

Appendix: Optimization Techniques 617

A.1 Introduction to Modeling and Optimization 617

A.2 Bayesian Analysis and the Prediction of Chemical Carcinogenicity 655

A.3 The Farmer’s Dilemma: Linear Model and Duality 657

A.4 Standard Normal Probability Table 664

References 665

Author Index 667

Subject Index 673

YACOV Y. HAIMES, PhD, is the Lawrence R. Quarles Professor at the School of Engineering and Applied Science, University of Virginia, USA, and is a member of the Systems and Information Engineering faculty and the Civil and Environmental Engineering faculty. He is the Founding Director (1987) of the university-wide Center for Risk Management of Engineering Systems. On the faculty of Case Western Reserve University, USA, for 17 years, he was the Chair of the Systems Engineering Department, and Director of the Center for Large-Scale Systems and Policy Analysis.